The vast wealth of electronic content available provides consumers with an astounding amount of available information. However, the great quantity of available information presents challenges in locating particular information that is of interest to a given person at a given time. While various search engines and cataloging systems facilitate finding desired information, unfortunately such search engines and cataloging systems typically base search results upon embedded metadata, text analytics, and the like. As such, while search results from search engines and cataloging systems may relate to the searched topic, the relevance to a person's particular interest may be lacking.
One attempt to improve the ability to identify and retrieve electronic content is the use of tags. Tags are generally a keyword used to describe or categorize a piece of content, such as a picture, webpage, electronic document, or the like. Tags represent an improvement over typical searching mechanisms in that the tag associated with a piece of content may be more focused or more relevant to the major and/or important aspects of the content. There are different types of tags, such as tags assigned by contributors or readers of data or content, sometimes the tags are ‘suggested’ by text analytics, and sometimes the tags are created via automated processes. The sources of these tags, can have distinct implications on results of searches conducted using tags. Current systems do not distinguish between tags created from different sources. By not distinguishing, the important implications to users and to search and analysis software are missed.